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Soft Computing

, Volume 22, Issue 16, pp 5429–5437 | Cite as

Credibility support vector machines based on fuzzy outputs

  • Chao Wang
  • Xiaowei Liu
  • Minghu Ha
  • Ting Zhao
Focus

Abstract

Support vector machines (SVMs) based on fuzzy theory have attracted widespread attentions in pattern recognition and machine learning. However, these SVMs have some limitation in dealing with some classification problems with fuzzy outputs, which results in the ignorance of the fuzziness of fuzzy outputs. Motivated by this, the possibility and necessity of fuzziness of fuzzy outputs are discussed, and the dynamic partitioning methods of these fuzzy output training samples are demonstrated based on credibility measure. Then, the corresponding dynamic credibility support vector machines based on fuzzy outputs are established, and the feasibility and effectiveness of credibility SVMs are shown by experimental results.

Keywords

Support vector machines Fuzzy outputs Credibility measure Confidence levels 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 11626079), the Natural Science Foundation of Hebei Province of China (No. F2015402033), and Scientific Research Project of Higher Education Institutions of Hebei Province(No. BJ2017031).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Intelligent Computing and Financial Security Laboratory, School of Management Engineering and BusinessHebei University of EngineeringHandanPeople’s Republic of China
  2. 2.School of ScienceHebei University of EngineeringHandanPeople’s Republic of China

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